class_docstr: This class implements a model-specific automatic speech recognizer using the end-to-end speech recognizer
  DeepSpeech and PyTorch. It supports both version 2 and version 3 of DeepSpeech models as released at
  https://github.com/SeanNaren/deepspeech.pytorch.
  
  | Paper link: https://arxiv.org/abs/1512.02595
docstr: Transform the user input space into the model input space.
  
  :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
            could have different lengths. A possible example of `x` could be:
            `x = np.ndarray([[0.1, 0.2, 0.1, 0.4], [0.3, 0.1]])`.
  :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
            lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
  :param compute_gradient: Indicate whether to compute gradients for the input `x`.
  :param tensor_input: Indicate whether input is tensor.
  :param real_lengths: Real lengths of original sequences.
  :return: A tuple of inputs and targets in the model space with the original index
           `(inputs, targets, input_percentages, target_sizes, batch_idx)`, where:
           - inputs: model inputs of shape (nb_samples, nb_frequencies, seq_length).
           - targets: ground truth targets of shape (sum over nb_samples of real seq_lengths).
           - input_percentages: percentages of real inputs in inputs.
           - target_sizes: list of real seq_lengths.
           - batch_idx: original index of inputs.
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code: def _transform_model_input(
      self,
      x: Union[np.ndarray, "torch.Tensor"],
      y: Optional[np.ndarray] = None,
      compute_gradient: bool = False,
      tensor_input: bool = False,
      real_lengths: Optional[np.ndarray] = None,
  ) -> Tuple[
      "torch.Tensor",
      "torch.Tensor",
      "torch.Tensor",
      "torch.Tensor",
      List,
  ]:
      """
      Transform the user input space into the model input space.
  
      :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
                could have different lengths. A possible example of `x` could be:
                `x = np.ndarray([[0.1, 0.2, 0.1, 0.4], [0.3, 0.1]])`.
      :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
                lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
      :param compute_gradient: Indicate whether to compute gradients for the input `x`.
      :param tensor_input: Indicate whether input is tensor.
      :param real_lengths: Real lengths of original sequences.
      :return: A tuple of inputs and targets in the model space with the original index
               `(inputs, targets, input_percentages, target_sizes, batch_idx)`, where:
               - inputs: model inputs of shape (nb_samples, nb_frequencies, seq_length).
               - targets: ground truth targets of shape (sum over nb_samples of real seq_lengths).
               - input_percentages: percentages of real inputs in inputs.
               - target_sizes: list of real seq_lengths.
               - batch_idx: original index of inputs.
      """
      import torch
      import torchaudio
      from deepspeech_pytorch.loader.data_loader import _collate_fn
  
      # Get parameters needed for the transformation
      if self._version == 2:
          window_name = self.model.audio_conf.window.value
          sample_rate = self.model.audio_conf.sample_rate
          window_size = self.model.audio_conf.window_size
          window_stride = self.model.audio_conf.window_stride
  
      else:
          window_name = self.model.spect_cfg["window"].value
          sample_rate = self.model.spect_cfg["sample_rate"]
          window_size = self.model.spect_cfg["window_size"]
          window_stride = self.model.spect_cfg["window_stride"]
  
      n_fft = int(sample_rate * window_size)
      hop_length = int(sample_rate * window_stride)
      win_length = n_fft
  
      # Get window for the transformation
      if window_name == "hamming":  # pragma: no cover
          window_fn = torch.hamming_window  # type: ignore
      elif window_name == "hann":  # pragma: no cover
          window_fn = torch.hann_window  # type: ignore
      elif window_name == "blackman":  # pragma: no cover
          window_fn = torch.blackman_window  # type: ignore
      elif window_name == "bartlett":  # pragma: no cover
          window_fn = torch.bartlett_window  # type: ignore
      else:  # pragma: no cover
          raise NotImplementedError(
              f"Spectrogram window {window_name} not supported."
          )
  
      # Create a transformer to transform between the two spaces
      transformer = torchaudio.transforms.Spectrogram(
          n_fft=n_fft,
          hop_length=hop_length,
          win_length=win_length,
          window_fn=window_fn,
          power=None,
      )
      transformer.to(self._device)
  
      # Create a label map
      label_map = {
          self._model.labels[i]: i
          for i in range(len(self._model.labels))
      }
  
      # We must process each sequence separately due to the diversity of their length
      batch = []
      for i, _ in enumerate(x):
          # First process the target
          if y is None:
              target = []
          else:
              target = list(
                  filter(
                      None,
                      [label_map.get(letter) for letter in list(y[i])],
                  )
              )
  
          # Push the sequence to device
          if isinstance(x, np.ndarray) and not tensor_input:
              x[i] = x[i].astype(config.ART_NUMPY_DTYPE)
              x[i] = torch.tensor(x[i]).to(self._device)
  
          # Set gradient computation permission
          if compute_gradient:
              x[i].requires_grad = True
  
          # Transform the sequence into the frequency space
          if tensor_input and real_lengths is not None:
              transformed_input = transformer(x[i][: real_lengths[i]])
          else:
              transformed_input = transformer(x[i])
  
          if self._version == 3 and packaging.version.parse(
              torch.__version__
          ) >= packaging.version.parse("1.10.0"):
              spectrogram = torch.abs(transformed_input)
          else:
              spectrogram, _ = torchaudio.functional.magphase(
                  transformed_input
              )
          spectrogram = torch.log1p(spectrogram)
  
          # Normalize data
          mean = spectrogram.mean()
          std = spectrogram.std()
          spectrogram = spectrogram - mean
          spectrogram = spectrogram / std
  
          # Then form the batch
          batch.append((spectrogram, target))
  
      # We must keep the order of the batch for later use as the following function will change its order
      batch_idx = sorted(
          range(len(batch)),
          key=lambda i: batch[i][0].size(1),
          reverse=True,
      )
  
      # The collate function is important to convert input into model space
      inputs, targets, input_percentages, target_sizes = _collate_fn(
          batch
      )
  
      return inputs, targets, input_percentages, target_sizes, batch_idx
  
imports: import logging
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  from pkg_resources import packaging
  import numpy
  from art import config
  from art.estimators.pytorch import PyTorchEstimator
  from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin, PytorchSpeechRecognizerMixin
  from art.utils import get_file
label_desc: Use image node_id 7 for calling the PyTorchDeepSpeech obj's underlying member method code with example usage: obj._transform_model_input(x, y, compute_gradient, tensor_input, real_lengths) and returns: inputs,  targets,  input_percentages,  target_sizes,  batch_idx